Fis, one of the most important nucleoid-associated proteins, functions as a global regulator of transcription in bacteria that has been comprehensively studied in Escherichia coli K12. Fis also influences the virulence of Salmonella enterica and pathogenic E. coli by regulating their virulence genes, however, the relevant mechanism is unclear. In this report, using combined RNA-seq and chromatin immunoprecipitation (ChIP)-seq technologies, we first identified 1646 Fis-regulated genes and 885 Fis-binding targets in the S. enterica serovar Typhimurium, and found a Fis regulon different from that in E. coli. Fis has been reported to contribute to the invasion ability of S. enterica. By using cell infection assays, we found it also enhances the intracellular replication ability of S. enterica within macrophage cell, which is of central importance for the pathogenesis of infections. Salmonella pathogenicity islands (SPI)-1 and SPI-2 are crucial for the invasion and survival of S. enterica in host cells. Using mutation and overexpression experiments, real-time PCR analysis, and electrophoretic mobility shift assays, we demonstrated that Fis regulates 63 of the 94 Salmonella pathogenicity island (SPI)-1 and SPI-2 genes, by three regulatory modes: i) binds to SPI regulators in the gene body or in upstream regions; ii) binds to SPI genes directly to mediate transcriptional activation of themselves and downstream genes; iii) binds to gene encoding OmpR which affects SPI gene expression by controlling SPI regulators SsrA and HilD. Our results provide new insights into the impact of Fis on SPI genes and the pathogenicity of S. enterica.

Understanding the interactions of soil microbial species and how they responded to disturbances are essential to ecological restoration and resilience in the semihumid and semiarid damaged mining areas. Information on this, however, remains unobvious and deficiently comprehended. In this study, based on the high throughput sequence and molecular ecology network analysis, we have investigated the bacterial distribution in disturbed mining areas across three provinces in China, and constructed molecular ecological networks to reveal the interactions of soil bacterial communities in diverse locations. Bacterial community diversity and composition were classified measurably between semihumid and semiarid damaged mining sites. Additionally, we distinguished key microbial populations across these mining areas, which belonged to Proteobacteria, Acidobacteria, Actinobacteria, and Chloroflexi. Moreover, the network modules were significantly associated with some environmental factors (e.g., annual average temperature, electrical conductivity value, and available phosphorus value). The study showed that network interactions were completely different across the different mining areas. The keystone species in different mining areas suggested that selected microbial communities, through natural successional processes, were able to resist the corresponding environment. Moreover, the results of trait-based module significances showed that several environmental factors were significantly correlated with some keystone species, such as OTU_8126 (Acidobacteria), OTU_8175 (Burkholderiales), and OTU_129 (Chloroflexi). Our study also implied that the complex network of microbial interaction might drive the stand resilience of soil bacteria in the semihumid and semiarid disturbed mining areas.

Synthesis of natural electric and magnetic Time series using Interstation transfer functions and time series from a Neighboring site (STIN) is a new approach for recovering natural electric and magnetic fields and reduce the influence of anthropogenic noise. The proposed approach modifies the windows of the local electric and magnetic time series that are affected by noise with time series resulted from modifying the spectra of the magnetic time series from a neighboring site with the interstation transfer functions between the local and neighboring sites. The STIN method was tested with artificially contaminated electric and magnetic time series. Comparison between STIN‐corrected time series and original noncontaminated time series shows high similarity, both in the time and frequency domains. Differences were quantified using the normalized root‐mean‐square error, the correlation coefficient, and the signal‐to‐noise ratio. The STIN method was also applied to two sites affected by unconstrained anthropogenic noise, thus demonstrating the ability and accuracy of STIN in synthesizing natural electric and magnetic fields and reducing the influence of anthropogenic noise. The synthesized time series provided by STIN show the method to be valuable for magnetotelluric (MT) geophysical applications, by increasing the reliability when constrains the MT impedance tensor and by reducing the scatter of data points when the time series are affected by noise, particularly for longer periods. As STIN is based on interstation transfer functions, the electric and magnetic time series can be treated independently, enabling computation of the MT impedance tensor even when the electric and magnetic time series of the local site were recorded at different times.